Reinforcement learning for optimal scheduling of Glioblastoma treatment with Temozolomide
Background: Glioblastoma multiforme (GBM) is the most frequent primary brain tumor in
adults and Temozolomide (TMZ) is an effective chemotherapeutic agent for its treatment. In …
adults and Temozolomide (TMZ) is an effective chemotherapeutic agent for its treatment. In …
[HTML][HTML] Deep neural networks for neuro-oncology: Towards patient individualized design of chemo-radiation therapy for Glioblastoma patients
Background and objectives Glioblastoma multiforme (GBM) is the most common and deadly
type of primary cancers of the brain and central nervous system in adults. Despite the …
type of primary cancers of the brain and central nervous system in adults. Despite the …
Personalized oncology with artificial intelligence: The case of temozolomide
N Houy, F Le Grand - Artificial intelligence in medicine, 2019 - Elsevier
Purpose Using artificial intelligence techniques, we compute optimal personalized protocols
for temozolomide administration in a population of patients with variability. Methods Our …
for temozolomide administration in a population of patients with variability. Methods Our …
On optimal temozolomide scheduling for slowly growing glioblastomas
B Segura-Collar, J Jiménez-Sánchez… - Neuro-Oncology …, 2022 - academic.oup.com
Background Temozolomide (TMZ) is an oral alkylating agent active against gliomas with a
favorable toxicity profile. It is part of the standard of care in the management of glioblastoma …
favorable toxicity profile. It is part of the standard of care in the management of glioblastoma …
Optimal dynamic regimens with artificial intelligence: The case of temozolomide
N Houy, F Le Grand - PLoS One, 2018 - journals.plos.org
We determine an optimal protocol for temozolomide using population variability and
dynamic optimization techniques inspired by artificial intelligence. We use a …
dynamic optimization techniques inspired by artificial intelligence. We use a …
Simulation-based optimization of radiotherapy: Agent-based modeling and reinforcement learning
Along with surgery and chemotherapy, radiotherapy is an effective way to treat cancer. Many
cancer patients take delivery of radiation. The goal of radiotherapy is to destroy the tumor …
cancer patients take delivery of radiation. The goal of radiotherapy is to destroy the tumor …
[HTML][HTML] A neuro evolutionary algorithm for patient calibrated prediction of survival in Glioblastoma patients
Background and objectives Glioblastoma multiforme (GBM) is the most common and
malignant type of primary brain tumors. Radiation therapy (RT) plus concomitant and …
malignant type of primary brain tumors. Radiation therapy (RT) plus concomitant and …
Designing optimal combination therapy for personalised glioma treatment
Background Like it happens in other tumours, glioma cells co-evolve in a microenvironment
consisting of bona fide tumour cells as well as a range of parenchymal cells, which produces …
consisting of bona fide tumour cells as well as a range of parenchymal cells, which produces …
Reinforcement learning-based control of tumor growth under anti-angiogenic therapy
P Yazdjerdi, N Meskin, M Al-Naemi… - Computer methods and …, 2019 - Elsevier
Background and objectives: In recent decades, cancer has become one of the most fatal and
destructive diseases which is threatening humans life. Accordingly, different types of cancer …
destructive diseases which is threatening humans life. Accordingly, different types of cancer …
Reinforcement learning-based control of drug dosing for cancer chemotherapy treatment
The increasing threat of cancer to human life and the improvement in survival rate of this
disease due to effective treatment has promoted research in various related fields. This …
disease due to effective treatment has promoted research in various related fields. This …